Back office ops · Production

Formula Bot cuts connector build time from one week to one and a half days with n8n backend orchestration

The problem

Formula Bot's backend was built on AWS Lambda and custom code, which proved opaque and fragile for a non-developer founder, with changes in one workflow breaking unrelated parts. Bubble's API connector imposed strict execution time limits that blocked high-value features like web scraping and PDF conversion, and each new data connector required a one-off build process.

First attempt

The initial AWS Lambda and custom code approach introduced serious friction and fragility that a non-developer founder could not confidently maintain. Bubble's strict API execution time limits made long-running AI reasoning and heavy data processing workflows impossible.

Workflow diagram · grounded in source
1
User connects data source
Trigger
A user uploads or connects a data source through the Bubble front end.
▾ source quote
“A user uploads or connects a data source through the Bubble front end.”
2
Orchestration agent routes request
Routing
A central orchestration agent built in n8n receives user requests and dynamically routes them to the correct connector workflow.
▾ source quote
“At the heart of the architecture is a central orchestration agent built in n8n. This agent receives user requests and dynamically routes them to the correct connector workflow.”
3
Credentials and schema retrieval
Integration
n8n retrieves credentials and metadata by calling Bubble's API, including schema information stored securely in Bubble.
▾ source quote
“n8n retrieves credentials and metadata by calling Bubble's API, including schema information stored securely in Bubble.”
4
AI query generation and execution
Ai action
An AI agent generates and executes the appropriate SQL or API queries based on the schema and user intent.
▾ source quote
“An AI agent generates and executes the appropriate SQL or API queries based on the schema and user intent.”
5
Results stored and returned
Output
Results are processed, transformed, and stored in AWS S3, then returned to the user as structured files.
▾ source quote
“Results are processed, transformed, and stored in AWS S3, then returned to the user as structured files.”
Reported outcome

With n8n running roughly 60 percent of its workflows, Formula Bot cut connector development time from about a week to around a day and a half.
David estimates 20 to 30 hours saved per month and hundreds of hours overall, and the platform can now run workflows lasting up to 10 minutes, enabling enterprise data integrations previously out of reach.

Reported metrics
Share of workflows running in n8n60 percent
Workflow template reuse rate90 percent
Connector development time before n8nabout a week
Connector development time with n8naround a day and a half
Show all 8 reported metrics
share of workflows running in n8n60 percent
workflow template reuse rate90 percent
connector development time before n8nabout a week
connector development time with n8naround a day and a half
developer time saved per month20 to 30 hours
developer time saved overallhundreds of hours
maximum workflow execution timeup to 10 minutes
business performanceGrowth, profitability, and product feedback are all up
Reported stack
n8nBubbleAWS S3BigQuerySnowflakeGoogle AnalyticsMicrosoft SQL
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Source
https://n8n.io/case-studies/formula-bot/
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Frequently asked questions

What did this team achieve with this AI workflow?

With n8n running roughly 60 percent of its workflows, Formula Bot cut connector development time from about a week to around a day and a half.

What tools did this team use?

n8n, Bubble, AWS S3, BigQuery, Snowflake, Google Analytics, Microsoft SQL.

What results were reported?

Share of workflows running in n8n: 60 percent; Workflow template reuse rate: 90 percent; Connector development time before n8n: about a week; Connector development time with n8n: around a day and a half (source-reported, not independently verified).

What failed first in this deployment?

The initial AWS Lambda and custom code approach introduced serious friction and fragility that a non-developer founder could not confidently maintain.

How is this back office ops AI workflow structured?

User connects data source → Orchestration agent routes request → Credentials and schema retrieval → AI query generation and execution → Results stored and returned.

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